Causal Inference Book

Causal Inference Book

A cohesive presentation of concepts of, and methods for, causal inference. Covers causal inference without models, causal inference with models, and causal inference from complex longitudinal data.

Tag(s): Statistics

Publication date: 05 Mar 2017

ISBN-10: n/a

ISBN-13: n/a

Paperback: 220 pages

Views: 2,970

Type: Textbook

Publisher: n/a

License: n/a

Post time: 07 Mar 2017 03:30:00

Causal Inference Book

Causal Inference Book A cohesive presentation of concepts of, and methods for, causal inference. Covers causal inference without models, causal inference with models, and causal inference from complex longitudinal data.
Tag(s): Statistics
Publication date: 05 Mar 2017
ISBN-10: n/a
ISBN-13: n/a
Paperback: 220 pages
Views: 2,970
Document Type: Textbook
Publisher: n/a
License: n/a
Post time: 07 Mar 2017 03:30:00
From the Book's Webpage:
Miguel Hernan wrote:My colleague Jamie Robins and I are working on a book that provides a cohesive presentation of concepts of, and methods for, causal inference. Much of this material is currently scattered across journals in several disciplines or confined to technical articles. We expect that the book will be of interest to anyone interested in causal inference, e.g., epidemiologists, statisticians, psychologists, economists, sociologists, political scientists, computer scientists… The book is divided in 3 parts of increasing difficulty: causal inference without models, causal inference with models, and causal inference from complex longitudinal data.

We are making drafts of selected book sections available on this website. The idea is that interested readers can submit suggestions or criticisms before the book is published. To share any comments, please email me or visit @causalinference on Facebook. To cite the book, please use “Hernán MA, Robins JM (2017). Causal Inference. Boca Raton: Chapman & Hall/CRC, forthcoming.”




About The Author(s)


Miguel Hernan is Kolokotrones Professor of Biostatistics and Epidemiology in the Department of Epidemiology and the Department of Biostatistics at Harvard T.H. Chan School of Public Health. His research and teaching are focused on methodology for causal inference, including comparative effectiveness research to guide policy and clinical decisions.

Miguel Hernan

Miguel Hernan is Kolokotrones Professor of Biostatistics and Epidemiology in the Department of Epidemiology and the Department of Biostatistics at Harvard T.H. Chan School of Public Health. His research and teaching are focused on methodology for causal inference, including comparative effectiveness research to guide policy and clinical decisions.


James Robins is Mitchell L. and Robin LaFoley Dong Professor of Epidemiology in the Department of Epidemiology and the Department of Biostatistics in Harvard T.H. Chan School of Public Health. The principal focus of Dr. Robins’ research has been the development of analytic methods appropriate for drawing causal inferences from complex observational and randomized studies with time-varying exposures or treatments.

James Robins

James Robins is Mitchell L. and Robin LaFoley Dong Professor of Epidemiology in the Department of Epidemiology and the Department of Biostatistics in Harvard T.H. Chan School of Public Health. The principal focus of Dr. Robins’ research has been the development of analytic methods appropriate for drawing causal inferences from complex observational and randomized studies with time-varying exposures or treatments.


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